{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程案例\n",
    "- Kaggle 上有这样一个比赛：[城市自行车共享系统使用状况](https://www.kaggle.com/c/bike-sharing-demand)\n",
    "- 数据提供两年每小时的租赁情况。训练集由每月前19天组成，测试集为每月的20日至月末。\n",
    "- 参赛者需预测在测试集中每个小时租赁的自行车总数。\n",
    "### 各变量意义\n",
    "- datetime: 时间\n",
    "- season：季节\n",
    "- holiday：节假日\n",
    "- workingday：工作日\n",
    "- weather：天气 \n",
    "  - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "  - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "  - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain +Scattered clouds\n",
    "  - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog \n",
    "- temp：温度\n",
    "- atemp：体感温度\n",
    "- humidity：湿度\n",
    "- windspeed:风速\n",
    "- casual：未注册用户租车数\n",
    "- registered：已注册用户租车数\n",
    "- count：租车总数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 12 columns):\n",
      "datetime      10886 non-null object\n",
      "season        10886 non-null int64\n",
      "holiday       10886 non-null int64\n",
      "workingday    10886 non-null int64\n",
      "weather       10886 non-null int64\n",
      "temp          10886 non-null float64\n",
      "atemp         10886 non-null float64\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float64\n",
      "casual        10886 non-null int64\n",
      "registered    10886 non-null int64\n",
      "count         10886 non-null int64\n",
      "dtypes: float64(3), int64(8), object(1)\n",
      "memory usage: 1020.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6493 entries, 0 to 6492\n",
      "Data columns (total 9 columns):\n",
      "datetime      6493 non-null object\n",
      "season        6493 non-null int64\n",
      "holiday       6493 non-null int64\n",
      "workingday    6493 non-null int64\n",
      "weather       6493 non-null int64\n",
      "temp          6493 non-null float64\n",
      "atemp         6493 non-null float64\n",
      "humidity      6493 non-null int64\n",
      "windspeed     6493 non-null float64\n",
      "dtypes: float64(3), int64(5), object(1)\n",
      "memory usage: 456.6+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 合并训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/f/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:6211: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atemp</th>\n",
       "      <th>casual</th>\n",
       "      <th>count</th>\n",
       "      <th>datetime</th>\n",
       "      <th>holiday</th>\n",
       "      <th>humidity</th>\n",
       "      <th>registered</th>\n",
       "      <th>season</th>\n",
       "      <th>temp</th>\n",
       "      <th>weather</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>workingday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.395</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.635</td>\n",
       "      <td>8.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.635</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.395</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.395</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    atemp  casual  count             datetime  holiday  humidity  registered  \\\n",
       "0  14.395     3.0   16.0  2011-01-01 00:00:00        0        81        13.0   \n",
       "1  13.635     8.0   40.0  2011-01-01 01:00:00        0        80        32.0   \n",
       "2  13.635     5.0   32.0  2011-01-01 02:00:00        0        80        27.0   \n",
       "3  14.395     3.0   13.0  2011-01-01 03:00:00        0        75        10.0   \n",
       "4  14.395     0.0    1.0  2011-01-01 04:00:00        0        75         1.0   \n",
       "\n",
       "   season  temp  weather  windspeed  workingday  \n",
       "0       1  9.84        1        0.0           0  \n",
       "1       1  9.02        1        0.0           0  \n",
       "2       1  9.02        1        0.0           0  \n",
       "3       1  9.84        1        0.0           0  \n",
       "4       1  9.84        1        0.0           0  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt = train.append(test)\n",
    "tt = tt.reset_index().drop('index', axis=1)\n",
    "tt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练集分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/f/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ee16f7a20>"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 时间处理\n",
    "# 增加两列，分别为日期和小时\n",
    "temp = pd.DatetimeIndex(train['datetime'])\n",
    "train['year'] = temp.year\n",
    "train['date'] = temp.date\n",
    "train['hour'] = temp.hour\n",
    "# 产生一个星期几的类别变量\n",
    "train['dayofweek'] = pd.DatetimeIndex(train.date).dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ee14db390>"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 一天中各时间段对count的影响\n",
    "sns.boxplot(train['hour'], train['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ee1097f28>"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 一周总各天对count的影响\n",
    "sns.boxplot(train['dayofweek'], train['count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 效果不明显，换个展现方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ee103c160>"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 一周中每天的count变化情况\n",
    "sns.pointplot(x='hour', y='count', hue='dayofweek', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ee0df3d30>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同月份对count的影响\n",
    "train['month'] = pd.to_datetime(train['datetime']).dt.month\n",
    "sns.boxplot(train['month'], train['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ee0ce9438>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 节假日对count的影响\n",
    "sns.pointplot(x='hour', y='count',hue='workingday', data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出workingday与count的关系非常像dayofweek与count的关系，二者可以删掉一个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3eef29c160>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 天气对count的影响\n",
    "sns.pointplot(x='hour', y='count', hue='weather', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3eeefe5978>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 季节对count的影响\n",
    "sns.pointplot(x='hour', y='count', hue='season', data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对于离散性变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3eeeda5470>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 皮尔逊系数\n",
    "cor=train[['temp', 'atemp', 'casual', 'registered', 'humidity','windspeed', 'count']].corr()\n",
    "sns.heatmap(cor, square=True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从以上得出：\n",
    "- temp和atemp变量高度线性相关，建模时取其一;\n",
    "- casual和registered的总和就是count，删除即可\n",
    "- humidity和windspeed变量和count相关性不高，直接删除\n",
    "+ 所以，确定建模的变量有 hour, year, workingday, holiday, season, weather, atemp, count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对合并数据做特征处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workingday</th>\n",
       "      <th>holiday</th>\n",
       "      <th>atemp</th>\n",
       "      <th>count</th>\n",
       "      <th>hour_1</th>\n",
       "      <th>hour_2</th>\n",
       "      <th>hour_3</th>\n",
       "      <th>hour_4</th>\n",
       "      <th>hour_5</th>\n",
       "      <th>hour_6</th>\n",
       "      <th>...</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>year_2012</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>weather_2</th>\n",
       "      <th>weather_3</th>\n",
       "      <th>weather_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.395</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.635</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.635</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.395</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.395</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   workingday  holiday   atemp  count  hour_1  hour_2  hour_3  hour_4  hour_5  \\\n",
       "0           0        0  14.395   16.0       0       0       0       0       0   \n",
       "1           0        0  13.635   40.0       1       0       0       0       0   \n",
       "2           0        0  13.635   32.0       0       1       0       0       0   \n",
       "3           0        0  14.395   13.0       0       0       1       0       0   \n",
       "4           0        0  14.395    1.0       0       0       0       1       0   \n",
       "\n",
       "   hour_6    ...      hour_21  hour_22  hour_23  year_2012  season_2  \\\n",
       "0       0    ...            0        0        0          0         0   \n",
       "1       0    ...            0        0        0          0         0   \n",
       "2       0    ...            0        0        0          0         0   \n",
       "3       0    ...            0        0        0          0         0   \n",
       "4       0    ...            0        0        0          0         0   \n",
       "\n",
       "   season_3  season_4  weather_2  weather_3  weather_4  \n",
       "0         0         0          0          0          0  \n",
       "1         0         0          0          0          0  \n",
       "2         0         0          0          0          0  \n",
       "3         0         0          0          0          0  \n",
       "4         0         0          0          0          0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.DatetimeIndex(tt['datetime'])\n",
    "tt['year'] = temp.year\n",
    "tt['hour'] = temp.hour\n",
    "tt = tt[['hour', 'year', 'workingday', 'holiday', 'season', 'weather', 'atemp', 'count']]\n",
    "# 对离散型变量做One-hot编码, 比如颜色红,黄, 蓝编码为[ [1,0,0], [0,1,0], [0,0,1] ]\n",
    "tt = pd.get_dummies(tt, columns=['hour'], prefix=['hour'], drop_first=True)\n",
    "tt = pd.get_dummies(tt, columns=['year'], prefix=['year'], drop_first=True)\n",
    "tt = pd.get_dummies(tt, columns=['season'], prefix=['season'], drop_first=True)\n",
    "tt = pd.get_dummies(tt, columns=['weather'], prefix=['weather'], drop_first=True)\n",
    "tt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建模预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/f/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:3697: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workingday</th>\n",
       "      <th>holiday</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hour_1</th>\n",
       "      <th>hour_2</th>\n",
       "      <th>hour_3</th>\n",
       "      <th>hour_4</th>\n",
       "      <th>hour_5</th>\n",
       "      <th>hour_6</th>\n",
       "      <th>hour_7</th>\n",
       "      <th>...</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>year_2012</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>weather_2</th>\n",
       "      <th>weather_3</th>\n",
       "      <th>weather_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.635</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.635</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   workingday  holiday   atemp  hour_1  hour_2  hour_3  hour_4  hour_5  \\\n",
       "0           0        0  14.395       0       0       0       0       0   \n",
       "1           0        0  13.635       1       0       0       0       0   \n",
       "2           0        0  13.635       0       1       0       0       0   \n",
       "3           0        0  14.395       0       0       1       0       0   \n",
       "4           0        0  14.395       0       0       0       1       0   \n",
       "\n",
       "   hour_6  hour_7    ...      hour_21  hour_22  hour_23  year_2012  season_2  \\\n",
       "0       0       0    ...            0        0        0          0         0   \n",
       "1       0       0    ...            0        0        0          0         0   \n",
       "2       0       0    ...            0        0        0          0         0   \n",
       "3       0       0    ...            0        0        0          0         0   \n",
       "4       0       0    ...            0        0        0          0         0   \n",
       "\n",
       "   season_3  season_4  weather_2  weather_3  weather_4  \n",
       "0         0         0          0          0          0  \n",
       "1         0         0          0          0          0  \n",
       "2         0         0          0          0          0  \n",
       "3         0         0          0          0          0  \n",
       "4         0         0          0          0          0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从处理过后的数据集中提取训练集和测试集，[0:10886]和[10886:]\n",
    "new_train = tt.iloc[:10886, :]\n",
    "# 对count+1,然后取对数\n",
    "y = np.log1p(new_train['count'])\n",
    "new_test = tt.iloc[10886:, :].drop('count',axis=1)\n",
    "new_train.drop('count', axis=1, inplace=True)\n",
    "x = new_train\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2.833213\n",
       "1        3.713572\n",
       "2        3.496508\n",
       "3        2.639057\n",
       "4        0.693147\n",
       "5        0.693147\n",
       "6        1.098612\n",
       "7        1.386294\n",
       "8        2.197225\n",
       "9        2.708050\n",
       "10       3.610918\n",
       "11       4.043051\n",
       "12       4.442651\n",
       "13       4.553877\n",
       "14       4.672829\n",
       "15       4.709530\n",
       "16       4.543295\n",
       "17       4.219508\n",
       "18       3.583519\n",
       "19       3.637586\n",
       "20       3.610918\n",
       "21       3.555348\n",
       "22       3.367296\n",
       "23       3.688879\n",
       "24       2.890372\n",
       "25       2.890372\n",
       "26       2.302585\n",
       "27       1.945910\n",
       "28       1.386294\n",
       "29       1.098612\n",
       "           ...   \n",
       "10856    6.265301\n",
       "10857    5.869297\n",
       "10858    5.594711\n",
       "10859    5.129899\n",
       "10860    4.890349\n",
       "10861    4.406719\n",
       "10862    3.737670\n",
       "10863    2.772589\n",
       "10864    1.386294\n",
       "10865    1.791759\n",
       "10866    2.079442\n",
       "10867    3.465736\n",
       "10868    4.727388\n",
       "10869    5.897154\n",
       "10870    6.520621\n",
       "10871    5.762051\n",
       "10872    5.105945\n",
       "10873    5.303305\n",
       "10874    5.468060\n",
       "10875    5.365976\n",
       "10876    5.389072\n",
       "10877    5.472271\n",
       "10878    5.814131\n",
       "10879    6.333280\n",
       "10880    6.345636\n",
       "10881    5.820083\n",
       "10882    5.488938\n",
       "10883    5.129899\n",
       "10884    4.867534\n",
       "10885    4.488636\n",
       "Name: count, Length: 10886, dtype: float64"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 尝试多元线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8540144165763579"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lmodel = LinearRegression()\n",
    "lmodel.fit(x, y)\n",
    "cross_val_score(lmodel, x, y, cv=5).mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2267929774010248"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lmodel.fit(x_train, y_train)\n",
    "pre = lmodel.predict(x_test)\n",
    "mean_squared_error(y_test, pre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 尝试随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "rfr = RandomForestRegressor(random_state=50, max_features='sqrt', oob_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='sqrt', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=True, random_state=50, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': array([200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212,\n",
       "       213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225,\n",
       "       226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238,\n",
       "       239, 240])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调参 这一步计算量好大\n",
    "para = {'n_estimators': np.arange(200, 241, 1)}\n",
    "rf = GridSearchCV(estimator=rfr, param_grid=para, cv=5)\n",
    "rf.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 222}"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9873139416135428"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(n_estimators=222, random_state=50, max_features='sqrt',oob_score=True)\n",
    "cross_val_score(rfr, x, y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.008629833667255199"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.fit(x_train, y_train)\n",
    "pre = rfr.predict(x_test)\n",
    "mean_squared_error(y_test, pre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从结果来看随机森林优于多元线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/f/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='sqrt', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=True, random_state=50, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "co = rfr.predict(new_test)\n",
    "m = []\n",
    "# 对结果做指数减一，取四舍五入\n",
    "for i in (np.exp(co) - 1):\n",
    "    n = round(i)  \n",
    "    m.append(n)\n",
    "predict = pd.DataFrame({'datetime': test['datetime'], 'count': m})\n",
    "predict.to_csv('rfr.csv', index=False)"
   ]
  }
 ],
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